Silver Bullet Or Trojan Horse? the Effects of Inclusionary Zoning on Local Housing Markets

FURMAN CENTER FOR REAL ESTATE & URBAN POLICY

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Silver Bullet or Trojan Horse? The Effects of Inclusionary Zoning on Local Housing Markets

W O R K I N G P A P E R 0 8 – 0 1

Jenny Schuetz · Rachel Meltzer · Vicki Been

Silver Bullet or Trojan Horse? The Effects of Inclusionary Zoning on Local Housing Markets

Jenny Schuetz* Economics Department City College of New York

Rachel Meltzer** Vicki Been Furman Center for Real Estate and Urban Policy New York University

June 9, 2008

Abstract

Many local governments are adopting inclusionary zoning (IZ) as a means of producing affordable housing without direct public subsidies. In this paper, we use panel data on IZ in the San Francisco metropolitan area and Suburban Boston to analyze how much affordable housing the programs produce and how IZ affects the prices and production of market-rate housing. The amount of affordable housing produced under IZ has been modest and depends primarily on how long IZ has been in place. Results from Suburban Boston provide some evidence that IZ has contributed to increased housing prices and lower rates of production. In the San Francisco area, there is no evidence of a statistically significant effect of IZ on housing prices or production.

This paper is based on a longer working paper version written with financial support from the Center for Housing Policy. We are grateful for extensive comments from Jeffrey Lubell, Victoria Basolo, David Crowe, Richard Green, Jonathan Levine, Paul Peninger, Kalima Rose, and participants in the NYU School of Law faculty brownbag. In addition we would like to thank Michele Chirco, Andrew Dansker, Ryan Downer, Lina Duran, Peter Madden, Carissa Mann and Yvonne Martinez for providing excellent research assistance. Joe Gyorko and Raven Saks generously provided historical data on building permits. All remaining errors or omissions are wholly the responsibility of the authors.

* 160 Convent Avenue, NAC 5/144, New York NY 10031. [email protected] ** 110 West Third Street, New York NY 10012. [email protected], [email protected]

Section 1: Introduction

Rising housing prices and rents in many metropolitan areas over the past decade have

drawn the attention of policymakers, housing advocates, the media and academics alike.

Although the causes of price inflation may differ by location, there is considerable evidence that

in some parts of the country, restrictive zoning and other land use regulations have contributed to

higher housing prices (see, for example, Fischel 1990; Glaeser, Gyourko and Saks 2005;

Malpezzi and Green 1996; Malpezzi 1996; Pollakowski and Wachter 1990; Quigley and Rafael

2004). Faced with rapidly rising prices of market-rate housing, stagnant real incomes for many

households, and limited availability of federal or state subsidies, local governments are actively seeking new policy tools to help low- and moderate-income households afford housing. One increasingly popular policy is local inclusionary zoning (sometimes called inclusionary housing or incentive zoning). Inclusionary zoning (IZ) programs either require developers to make a

certain percentage of the units within their market-rate residential developments available at

prices or rents that are affordable to specified income groups, or offer incentives that encourage them to do so. Despite the growing popularity of IZ among policymakers, there has been almost no empirical research on the effects of these programs, either about how much affordable housing they actually produce, or about their broader impacts on the price and quantity of market-rate housing. This study seeks to fill this gap in the literature by examining IZ programs in two regions in which IZ is relatively widespread and of long duration: the San Francisco metropolitan area and the Boston-area suburbs.

IZ has become a controversial topic, with avid supporters and critics. Many economists and developers believe that IZ imposes additional costs on new residential development, and as such predict that it will constrain the supply and increase the price of housing in jurisdictions that adopt it. Affordable housing advocates counter that IZ can be an effective means of producing

below-market rate units that would not otherwise be produced and that, unlike traditional

affordable housing programs, it does not require direct public subsidies and produces affordable units in a geographically dispersed pattern. Due in large part to the paucity of data describing IZ programs, very little objective empirical research has been done to test the validity of any of

these claims.

In this study, we present empirical evidence of the effects of IZ on local housing markets.

We have assembled panel data sets for the San Francisco metropolitan area and Suburban

Boston, including characteristics of IZ programs derived from several surveys of local IZ

programs, housing prices, new residential construction permits and standard determinants of

housing market supply and demand (such as demographics and existing housing stock). We also

have data on some other types of land use regulations, such as growth controls, as well as data on

affordable units produced under the Low Income Housing Tax Credit program. For each region,

we conduct regression analysis to determine what IZ program characteristics and housing market

conditions affect the production of affordable housing under IZ and how IZ programs have

affected the price and production of market rate single-family housing.

The empirical analysis suggests that the ideological debate over IZ has greatly

exaggerated both the benefits and the dangers of IZ: any negative effects on housing prices and

production have been relatively modest, but only modest amounts of affordable housing have

been produced through IZ programs. The most robust determinant of the amount of affordable housing produced is the number of years IZ has been in place. The San Francisco results also

suggest that more flexible programs have produced more affordable units. Our findings

regarding the effects of IZ programs on housing permits and prices are somewhat mixed. The

results from the Boston-area suburbs suggest that IZ may constrain housing production and that

prices tend to be higher in jurisdictions with IZ. The results from San Francisco do not reveal

significant effects on housing prices or production.

The remainder of this study is organized as follows. Section 2 summarizes previous

empirical research; Section 3 lays out theoretical predictions about the impacts IZ will have on

housing production and prices; Section 4 provides background and descriptive statistics on IZ

programs in each region; Section 5 discusses our empirical strategy and describes our data;

Section 6 presents findings of regression analysis; and Section 7 concludes.

Section 2: Previous empirical research

Although there is a fairly extensive literature on the economic and legal theory of

inclusionary zoning, to date there has been essentially no rigorous empirical analysis of the effects of inclusionary zoning on housing supply. The most widely cited attempts to determine

the effects of IZ are a pair of studies of California cities and counties by Powell and Stringham for the Reason Foundation (2004a and 2004b). They define the “cost” of each affordable unit as the difference between the average market price in the jurisdiction and the maximum affordable price allowed under IZ; by their calculations, the median cost of each affordable unit across all cities was $346,212. Powell and Stringham also assess the impact of IZ on production levels by comparing the average number of housing permits issued in cities with IZ over several time intervals before and after the adoption of the ordinance; on average, permits declined by 31 percent in the seven years after IZ was adopted. However, as critics have pointed out (Basolo and Calavita 2004), Powell and Stringham’s work relies on several questionable assumptions.

For instance, the cost differential assumes that in the absence of IZ policies, the same total number of units would have been constructed and all units would have sold for the average

market price. Moreover, the study provides no evidence on changes in housing prices and new permits in California jurisdictions without inclusionary zoning over the same time period, so it is

unclear whether the decline in permitting is due to IZ or to exogenous contemporary changes that

affect all jurisdictions. In short, the results of the two studies should be interpreted only as

descriptive, not as proof of a causal relationship between IZ and housing market outcomes.

More recently, Knaap, Bento and Lowe (2008) completed a study looking at the impact

of IZ programs on the production and prices of housing in California. Controlling for year and

city-specific fixed effects, they estimate the impact of IZ adoption on housing permits for single-

and multi-family structures, and find that IZ has no significant effect on the number of housing

permits for either structure type. However, they find that single-family housing permits as a

share of total permits are seven percentage points lower in jurisdictions with IZ than those

without IZ. The decreased share of single-family permits is even more pronounced for IZ

jurisdictions with lower project size threshold levels and higher required shares of affordable

units. To estimate the effect of IZ on housing prices and size, Knaap et al. estimate property-

level hedonic regressions that control for property characteristics, the year and quarter of the

sale, and the local school district and neighborhood. They find that in jurisdictions with IZ,

housing prices increase, on average, by 2.2 percent. This effect, however, is different for high-

and low-priced houses: IZ programs actually lower the price by about 0.8 percent for houses

below median price and raise prices by about 5 percent for above-median priced houses. Their

results also suggest IZ programs decrease the mean single-family housing size by approximately

48 square feet, particularly for houses below the median price.

The paucity of rigorous empirical research on the effects of IZ is due in large part to the

difficulty of obtaining accurate data on the presence and characteristics of inclusionary zoning

programs across jurisdictions and over time, as well as units produced under such programs. To

predict how inclusionary zoning might affect the supply and price of housing, however, we can draw upon some findings from empirical studies of similar forms of land use regulation, although with some caveats about the comparability of the programs. Below we review empirical research on the effects of related land use regulations, specifically impact fees and statewide “fair share” housing requirements.

The most recent empirical studies of the effects of impact fees find that housing prices

rise with the imposition of impact fees. Delaney and Smith (1989a, 1989b) were the first to empirically measure the effect of impact fees on the prices of existing and new housing. They look specifically at one jurisdiction, Dunedin, FL, over a period of 12 years and find significantly

higher housing prices in Dunedin relative to two of three non-fee control communities. These differences, however, disappear after about seven years into the study period. A series of studies followed, many of which do find empirically sound evidence of price increases (see, for instance,

Baden and Coursey 1999; Mathur, Waddell and Blanco 2004 and reviews of other studies summarized by Been 2005 and Evans-Cowley and Lawhon 2003). However, it is unclear what drives housing prices to increase: the added value from infrastructure/public services made

possible by the fees, or a possible supply constraint due to the tax. How land prices are affected is less definitive in the literature (Nelson and Lillydahl 1992; Skaburskis and Qadeer 1992); however a more recent study by Ihlanfeldt and Shaugnessy (2004) improves upon many of the limitations of previous investigations and finds significant reductions in land prices. With regard to housing production, the empirical results are also mixed. Skidmore and Peddle (1998) found a significant negative correlation between impact fees and the number of new homes built. On the other hand, Burge and Ihlanfeldt (2006) find no discernable effect of impact fees on number of

single-family home completions. The theoretical prediction about how impact fees would affect

completions is ambiguous: impact fees increase developer costs, but may also increase rates of

project approval by local governments (see also Mayer and Somerville 2000). Given the

theoretical differences between impact fees and IZ – impact fees (in theory) are used to pay for services enjoyed by new homeowners who pay the fees, while most new residents in

jurisdictions with IZ do not live in the affordable units – and the jurisdiction-specific evidence,

it is unclear how much can be extrapolated from these findings.

Another conceptually similar set of policies, albeit on the state level, are regional “fair

share” arrangements, under which each locality is required to provide some predetermined

proportion of the region’s low-income housing. The state with the oldest and best known such

policy is New Jersey (developed in response to the series of Mount Laurel court decisions). In

New Jersey, communities must develop a state-certified plan to reach their fair share obligation

through one or more of the following tools: building or rehabilitating low-income housing

directly, paying other communities within the region to provide up to 50 percent of their housing

obligation, or allowing developers to build at higher densities in exchange for developing affordable units. A study conducted approximately 5 years after the state law went into effect showed that over half of the 59 municipalities with certified housing plans had some density bonus provision, and nearly 60 percent of the units built were through a density bonus (Rubin et

al. 1990). Assuming that municipalities adopt plans that minimize the cost of meeting their

obligations, this can be viewed as indirect evidence that voluntary density bonuses are more

efficient means of producing affordable units than the other two tools. However there are

significant differences in choice of tools across municipalities, reflecting variation in resident

preferences and/or development costs; places that had higher initial housing densities were less

likely to adopt density bonuses, and more affluent communities were more likely to pay other jurisdictions to provide their allotment. Thus the presence and structure of inclusionary zoning ordinances is clearly endogenous and must be treated accordingly in empirical analysis.

Section 3: What are the predicted impacts of IZ on housing supply?

Mandatory IZ programs are essentially a tax on new residential development (Been 1991,

Clapp 1981, Ellickson 1981), and as such, we would expect them to raise the prices and reduce the quantity of housing. The size and incidence of the impacts will depend on a variety of factors, including the stringency and structure of the IZ program, the stringency of other types of land use regulations, and the relative elasticities of housing supply and demand. In this section, we discuss some predicted effects of IZ on housing supply, based on standard models of urban economics and public finance.

Under traditional IZ programs, a proposal for new residential development triggers a requirement to produce a specified share of units that will be sold or rented at a set price/rent that is below the market price/rent for that unit.1 Because developers will receive lower revenues on the affordable units, they are likely to earn lower total profits than in the absence of IZ. In response, developers may choose not to build in jurisdictions with IZ, unless they are able to offset their lost revenues on the affordable units either by raising prices on market-rate units or paying lower prices for land. The extent to which a developer can raise prices on market-rate units will depend on a number of factors, including the relative elasticities of supply and demand

(discussed in more detail below) and whether alternative land uses (other types of residential or non-residential development) face similar taxes. Because fewer households are willing to pay for higher priced units, this implies that lower numbers of units will be produced, both by an

1 We begin by discussing mandatory IZ programs and later discuss different implications for voluntary IZ programs.

individual developer and in the aggregate. Assuming that both developers and households are

mobile, some of the IZ tax will likely be capitalized into decreased values of residential land. At

lower prices, fewer landowners will be willing to sell, so lower land prices also imply lower

levels of housing production. By acting as a constraint on new supply, this type of IZ policy is

likely to increase the prices of existing housing in the jurisdiction as well the price of new units

constructed.

The size of the effective tax imposed by IZ, and thus the size of the impacts on housing

and land prices and housing production, will depend in large part on the stringency and

characteristics of the IZ program. IZ ordinances can be structured in an almost infinite number

of ways, with various implications for stringency. Below we consider how, in theory, several

key characteristics are likely to affect the size of impacts on the price and production of market

rate housing; in Section 4, we describe the actual characteristics of IZ programs in our two study

areas.

One of the essential characteristics of IZ programs is whether they are mandatory,

requiring developers to set aside below-market rate units, or voluntary, offering incentives for

developers to participate. All else equal, mandatory programs will clearly be more restrictive

and are likely to have larger impacts on housing supply than voluntary programs. A second key

characteristic is the breadth of applicability of IZ. Some IZ programs are written to apply broadly to most residential developments, while other programs grant exemptions for certain

projects or types of development, such as projects with small number of units, particular tenure or structure types. The greater the number of residential projects that are exempted from IZ, the less stringent the program will be, and the smaller the size of the effective tax, compared to a program with no exemptions. Exemptions may also encourage gaming by developers, such as

proposing developments just under the size threshold that triggers IZ. Many IZ programs offer some type of cost offset to the developer, such as density bonuses or fast-track permitting. With a density bonus, developers are allowed to build a larger number of units on a given parcel than would be allowed under conventional zoning. The larger the number of additional units allowed under the density bonus, the greater the offsetting profit for the developer and the smaller the

effective tax imposed by IZ. A fourth characteristic of IZ programs is the availability of buyout

options, that is, alternatives to building below-market rate units on site. The most commonly

granted alternatives are permission to produce the required affordable units at a different location

within the jurisdiction, allowing developers to pay cash in lieu of development, or allowing developers to donate land intended for future affordable housing. If the buyout options are set at lower costs than on-site development (for instance, the amount of cash per unit is less than the cost of developing units), then granting buyout options can lower the size of the effective tax

imposed by IZ. IZ programs also vary in the share of total units that must meet affordability

restrictions; the larger the required share, the higher the effective size of the tax and the larger the

impacts on housing prices and production. Most programs specify the income of the target

population, for instance, low income versus moderate income households. Setting a lower

income target implies greater reductions in developer profits and a larger effective tax. Finally,

IZ programs may specify that the affordability restrictions be in place for different lengths of

time. The length of affordability restrictions may have somewhat different impacts depending on

whether the program primarily affects rental or owner-occupied units, but in general, we assume

that longer periods of cost restrictions are more restrictive.

Because IZ ties affordable housing production to production of market-rate housing, the

number of affordable units that will be produced under IZ also depends on the size of the tax. In

particular, if highly stringent IZ programs greatly reduce the amount of new market-rate housing

developed, then they may produce relatively few units. All of the characteristics that affect the

stringency of IZ programs thus have implications for the programs’ success at producing

affordable units. In theory, voluntary IZ programs that offer very attractive cost offsets to

developers to participate could result in greater numbers of affordable units than a highly

stringent mandatory program, while also avoiding the negative impacts on price and production

of market-rate housing. Many IZ advocates claim that voluntary programs are seldom used and

produce few affordable units, although this is not consistent with our data.2

In addition to the structure and characteristics of the IZ program, the anticipated effects

on housing and land prices and the quantity of new housing produced also depend on the

elasticities of housing supply and demand. The relative elasticities also will determine the

incidence of any effects. The elasticity of supply depends on standard supply-side variables,

such as physical or regulatory constraints on developable land, the relative cost of non-residential

development, including land costs, zoning, and the appropriateness of location (Clapp 1981, Katz

& Rosen 1987). Any factors that reduce the relative cost of non-residential development will

increase the likelihood that an IZ program will cause landowners and developers to shift away

from residential uses, so that the burden of IZ will fall more on homebuyers or renters. The

elasticity of demand will depend on income and preferences of new households, particularly their

willingness to pay to live in a particular jurisdiction (Dietderich 1997). Location-specific

amenities or institutions may increase willingness to pay the higher taxes imposed by IZ

2 In Massachusetts, among the 26 jurisdictions that have had IZ programs in place for at least two years and that reported whether IZ had produced any affordable units, half of the purely optional programs had produced some affordable housing, as had half the purely mandatory programs. Three of the four California jurisdictions with voluntary IZ reported having produced at least 200 units of affordable housing each (compared to a median of 78 units for mandatory programs), while the fourth voluntary program has been in place only since 2001 and did not report how many units have been built.

(Ellickson 1981). For instance, two of the jurisdictions in our sample with mandatory IZ

programs are Palo Alto and Cambridge; the presence of relatively immobile academic

institutions whose students and faculty may place a premium on proximity to the university,

along with closely related private-sector firms, may result in relatively inelastic demand for those

jurisdictions, allowing developers to pass along cost increases to consumers and decrease

production by relatively little. It is unclear how many jurisdictions, beyond the examples given,

have such inelastic demand that they can absorb IZ with little decrease in production. In general,

anything that decreases the relative price or increases the relative attractiveness of nearby

jurisdictions will decrease households’ willingness to bear taxes and shift the burden towards landowners and developers. In addition, if supply is relatively inelastic (for instance, developers

would face high barriers to transferring business to other locations), then more of the costs of IZ will be borne by developers than consumers. Moreover, there are likely to be spillover effects from surrounding jurisdictions; the prevalence of IZ, other affordable housing production programs and other land use regulations in neighboring jurisdictions will affect the ability of both developers and households to substitute away from jurisdictions with IZ.

Section 4: Characteristics of IZ in San Francisco and Suburban Boston Areas

The structure and details of IZ programs vary widely across jurisdictions, reflecting local differences in policy goals, housing market conditions and political circumstances. The ways in which IZ programs are structured and implemented also are likely to vary systematically across states, in response to the amount and type of authority over land use policy granted to local governments by the states, as well as differences in the states’ land use programs and initiatives to produce affordable housing. In the previous section, we discussed how several of the key

characteristics of IZ programs, including mandatory status, exemptions and cost offsets, can

affect the stringency of the program and thus the size of the impacts on housing prices and

production levels. In this section we briefly describe the characteristics of IZ programs adopted

by jurisdictions in the San Francisco metropolitan area and the Boston-area suburbs. In addition,

we summarize several state-specific laws and policies that could affect incentives and the ability

of local governments to adopt and enforce IZ programs. Variation in such laws across states

makes it difficult to compare the outcomes of IZ across our two regions.

State regulatory environments and related policies

Housing costs in California and Massachusetts are among the most expensive in the nation, and researchers have singled out both states as having some of the most stringent land use

regulations in the country (Glaeser, Schuetz and Ward 2006; Gyourko, Saiz and Summers 2006).

In California, counties and cities are responsible for adopting and enforcing zoning and other

forms of land use regulation, while city and town governments have jurisdiction over zoning in

Massachusetts (all land in the state is incorporated within city and town boundaries). Perhaps because of the high level of housing costs, both states have a number of statewide policies and programs to encourage the development of below-market rate housing, described in more detail below.

California has several state laws or policies that encourage or enable affordable housing development outside local IZ programs. Since 1979, state law has required that each city or county provide density bonuses and incentives to developers seeking to build affordable or age- restricted housing.3 The state mandate essentially creates a voluntary IZ program in jurisdictions

that have not adopted a local IZ ordinance. Interviews with local officials suggest that the state

3 To qualify as affordable, a proposed development must include at least 10% low income housing, 5% very low income housing, with affordability restrictions for at least 30 years Cal. Gov. Code §65915 (2007) (this statute is part of the chapter entitled “Density Bonuses and Other Incentives”)

law is not widely understood and is infrequently invoked by developers (Furman Center 2007).

A second related policy is the state’s mandate that counties and cities submit a general plan for their long-term physical development. The general plan must contain a housing element, to be reviewed at least every five years, which outlines a plan to provide “decent” housing for “people

of all economic means”.4 A third mechanism for providing affordable housing under the state’s

legal framework is the designation of Redevelopment Agencies to oversee construction in

blighted areas.5 These agencies receive a portion of the incremental taxes from newly

redeveloped areas that can be used to subsidize affordable housing. There is no systematic data on the production of affordable units under any of the three state programs; however staff in

several jurisdictions mentioned having negotiated the inclusion of affordable units on a case by

case basis prior to having adopted IZ. In some cases, such as Contra Costa County, these

alternative mechanisms may have resulted in development of a significant number of units

(Furman Center 2007).

Similarly, Massachusetts has several state laws that could supplement or replace local IZ

programs. The oldest of these, Chapter 40B, allows developers to apply under an expedited

process for a permit to build housing that does not conform to local zoning, if a minimum

percentage of the housing units are affordable to low- and moderate-income households. If the

developer’s application is denied by the local Zoning Board of Appeals, the state Housing

Appeals Committee can override the Board’s decision and order the issuance of the permit

(Massachusetts Department of Housing and Community Development 2004). Chapter 40B is

sometimes used by not-for-profit organizations to develop projects that are entirely affordable

(usually including state or federal subsidies), but it is also frequently used by for-profit

4 Cal Gov. Code at §65580, See also 66 Cal. Jur. 3d §33 5 Several of the interviewees in the Furman Center’s survey mentioned this as a method by which the state encourages the production of affordable housing.

developers who wish to build at higher densities than would be allowed under conventional

zoning, similar to voluntary IZ programs. Communities are only subject to Chapter 40B if less

than 10% of their existing stock meets state affordability criteria. A review of selected recent

master plans suggests that many communities adopt IZ in order to increase production of affordable housing, up to their 10% quota, in a manner perceived as giving more local control than 40B developments. However, for communities that have learned to manage the 40B

process to their liking (i.e. have good relationships with selected affordable housing developers),

the state law may reduce the incentive to adopt some form of IZ.6 Unfortunately, there is no

reliable 40B data available to test the relationship between IZ and 40B production. Two related

laws, adopted in 2007 and known as Chapter 40R and 40S, create incentives for localities to

increase allowable density in designated “smart growth” districts, but are too new to impact our

analysis.

Data sources

Data on the presence and characteristics of inclusionary zoning in the Bay Area were

assembled from a variety of different sources. The primary source is a survey conducted in 2002

by the California Coalition for Rural Housing (CCRH) and Nonprofit Housing Association of

California (NPH). Because that survey did not obtain complete data on several key variables, including the date of IZ adoption, mandatory status and the presence of density bonuses, the

Furman Center conducted a supplementary telephone survey in June 2007 with municipal

officials in approximately 35 jurisdictions.7 We then compared our dataset against several

additional sources: a 1994 survey conducted by Calavita and Grimes; a list of IZ programs

6 For more discussion and analysis of Anti-Snob laws in Massachussetts, Rhode Island and Connecticut, see S. Cowan, 2006, “Anti-Snob Land Use Laws, Suburban Exclusion, and Housing Opportunity,” Journal of Urban Affairs, 28 (3): 295-313. 7 More information about the survey, including the survey instrument and list of officials interviewed, can be found at www.furmancenter.nyu.edu/publications/documents/IZDraftfinal.pdf

reported by Vandell (2003), originally compiled by Rusk (2003); a new Inclusionary Housing

Policy database released in the summer of 2007 by CCRH; and a 2007 report by NPH, CCRH

and several other organizations.8 The various sources contain a number of discrepancies even on basic facts such as the year IZ was adopted. It is unclear whether such discrepancies result from changes in program characteristics over time (for instance, changing from an informal to an official IZ policy, or a major revision in the law), differences in the surveys and respondents or simply reporting errors. We have attempted to reconcile the discrepancies for the year of IZ adoption by choosing the earliest date corroborated by at least two of the sources referenced above.

All data on inclusionary zoning in Massachusetts are taken from the Local Housing

Regulation Database, compiled in 2004 by the Pioneer Institute for Public Policy and the

Rappaport Institute for Greater Boston.9 Most variables were coded directly from bylaws or

ordinances; information on production of affordable units under IZ was obtained from telephone

and email communication with municipal staff and cannot be independently verified.

Characteristics of IZ programs in both regions

The structure and characteristics of IZ programs across the two regions have both

similarities and differences, as shown in Table 1. IZ has been widely adopted by local

governments in both regions. As of 2006, forty-eight percent of jurisdictions in Bay Area had adopted IZ, representing 51% of population and 50% of land area. In Suburban Boston, 53% of cities and towns, comprising 58% of population and 55% of land area, were covered by IZ as of

2005. In general, IZ programs took hold earlier in the Bay Area: half the IZ programs in the San

8 According to the most recent survey, 77 jurisdictions in the Bay Area had adopted IZ as of 2006. We use the 55 jurisdictions identified in the earlier survey for our analysis, since the most recent programs are too new to have produced measurable effects. 9 More information on the development of the database, and downloadable data, can be found at www.pioneerinstitute.org/municipalregs/.

Francisco MSA were adopted before 1992, while half of the Boston-area programs have been

adopted since 2001.

Along several of the dimensions measured, IZ programs in the Bay Area appear to be

more stringent than those in Suburban Boston. Over 90% of Bay Area IZ programs (including

all of the counties) are mandatory, compared to 58% of programs in Suburban Boston. Perhaps

the most striking difference is the breadth of applicability: in the Bay Area, most IZ programs are

written to apply broadly to all residential development, with only a few exemptions for very

small projects (fewer than 5 units). By contrast, a large majority of IZ programs in Suburban

Boston apply only under a fairly narrow set of circumstances, for instance, to developments in

specific zoning districts or certain structure types (generally multifamily). Although it is difficult

to determine what share of proposed developments would actually trigger the IZ requirements in

any jurisdiction, at least in theory, the more narrowly written programs in Suburban Boston are

likely to affect fewer developments. Perhaps offsetting the difference in breadth of applicability,

however, 86% of IZ programs in the Bay Area include a variety of buyout options for

developers, most commonly in-lieu fees or off-site construction. Only 38% of the IZ programs in Suburban Boston (but more than half the mandatory programs) offer buyout options.

IZ programs across the two regions differ less on several other characteristics. The median share of units required to be set at below-market rents/prices in both regions is 15%; most Bay Area jurisdictions require either 10% or 15%, while Boston-area IZ programs have much higher variance on this dimension, with some programs requiring that up to one-half of units meet income targets. Bay Area programs are more likely to require that some units meet affordability targets for very low income households, although in both regions some mixture of low- and moderate-income households is the norm. Roughly similar shares of programs across

the regions offer density bonuses (67% in the Bay Area and 71% in Suburban Boston).

Affordability restrictions are generally shorter in the Bay Area, with a median of 45 years. One-

third of programs in Suburban Boston require permanent or very long-term restrictions (80 or

more years), although half the programs either do not specify a set term or use ambiguous

language (“as long as allowable under state law”).

Production of affordable housing under IZ shows considerable variation both within and

across regions. Nearly all jurisdictions in the Bay Area reported that at least some affordable

units have been developed as a result of the IZ program. Summing across all jurisdictions and all

years, IZ has yielded an estimated 9154 units in the Bay Area through 2003, with median annual

production of 15 units per year for counties, and 6 units per year for cities. Given the available

data, it is difficult to draw exact comparisons with production levels in the Suburban Boston

programs, but it appears that IZ has produced relatively little affordable housing so far.

According to reports by municipal staff, 43% of communities with IZ programs reported that no affordable units had been produced as of December 2004. In addition, over one-third of

communities were unable to state whether any affordable units had been built. The lack of

production may reflect the very recent dates of adoption in many communities, however.

Section 5: Empirical strategy and data description

Using data on IZ in the San Francisco metropolitan area and Suburban Boston, we

examine what affects the amount of affordable housing produced under IZ, and how IZ has

affected the price and production of market-rate housing. In this section, we describe in greater

detail the empirical strategy and data used to analyze each of these questions.

5.1 What affects the quantity of affordable housing produced under an IZ program?

The potential costs of IZ are the predicted negative impacts on housing markets

(increased prices or decreased production), while production of affordable units is the primary

potential benefit. We would expect various structural components of IZ (such as whether it

offers density bonuses) and the length of time IZ has been in place to affect the amount of

affordable housing produced under the program. Market pressures on housing supply and

demand that affect production of market-rate housing should also affect production of affordable

units. The specification of the model to be estimated varies somewhat for each region,

depending both on data availability and the nature of IZ programs. All of the jurisdictions in the

San Francisco area that have IZ have produced at least some affordable units, and we have

obtained at least rough estimates of the number of units produced. However, in the Boston

suburbs, many of the programs have never been triggered, and data on the number of units

produced (if any) are not exact. Equation 1 shows the general specification to be estimated for

San Francisco; equation 2 shows the model to be estimated for Suburban Boston. The reasons

for the different models are described in more detail below.

(1) IZ _ unitsit = f (IZ _ structureit , IZ _ yearsit , X it−1 )

(2) Pr[IZ _ usedit ] = f (IZ _ structureit , IZ _ yearsit , X it−1 ,Own _ regsit )

10 where IZ_unitsit is the number of affordable units built under IZ in jurisdiction i by time t,

Pr[IZ_usedit] is a binary variable indicating whether any affordable units have been built in

jurisdiction i at time t, IZ_structureit is a vector of variables describing the characteristics of the

IZ program, IZ_yearsit is a set of dummy variables indicating the length of time since IZ was

adopted, Xit-1 is a vector of housing supply and demand determinants in jurisdiction i at time t-1,

10 Time t is the year in which the survey of IZ programs was conducted, and is constant for all jurisdictions within an MSA but differs across MSAs.

and Own_regsit is a vector of variables measuring other types of land use regulations in jurisdiction i in time t. Structural characteristics of the IZ program are observed at a single point

in time, concurrent with production levels, and for the analysis are assumed to have remained

constant since the date of adoption. However, we know anecdotally that at least some places

have substantially amended their IZ programs since original adoption; changes in the stringency of IZ components since adoption will introduce noise into the estimated coefficients on the structural characteristics.11 Further descriptions of the variables are shown in Table 2.

Analysis of affordable housing production under IZ in the Boston suburbs raises two

empirical challenges. First, adoption of IZ in the Boston area is relatively recent; as shown in

Table 1, nearly half the IZ programs in the database were adopted after 2001. Not surprisingly, a

majority of the newer programs (27 of 48) reported that IZ has not been triggered (or used

voluntarily) as of the survey date. It will be difficult to determine whether this results from

structural reasons, market pressures or simply program duration. In particular, it will be difficult

to assess the effect of mandatory status (which theoretically is one of the more important

characteristics), because over half of the mandatory programs have been adopted since 2000 and

likely have not existed long enough to produce either affordable units or significant effects on

housing markets. The small sample of programs that has existed long enough to have produced

affordable units limits our ability to conduct fine-grained analysis of the relevance of program

characteristics, and may bias estimated effects of IZ on housing markets towards zero. The

second concern is that roughly one-third of jurisdictions with IZ did not report whether IZ had

ever been applied while 17 percent did not report the year IZ was adopted. Excluding

observations with missing data, particularly year adopted, seems unlikely to generate much bias

11 There is no evidence of any systematic pattern: some places have increased stringency over time while others have relaxed it.

in our results, but the missing data do further reduce our sample size, which will tend to increase standard errors and reduce significance levels.12

The analysis of affordable housing production under IZ in Suburban Boston is measured as a binary outcome – whether any affordable units have been built, for the reasons just described. In the San Francisco area, on the other hand, all jurisdictions with IZ have produced at least some affordable housing, so we can estimate the effect of structural and market dynamics on the number of affordable housing units produced.13 Many IZ programs in the Bay Area have existed longer than those in Massachusetts, but only 55 jurisdictions had IZ as of 2006, yielding quite a small sample for statistical analysis. Of those 55 IZ programs, only four are optional, so it is not possible to test for statistically significant differences between mandatory and optional programs. Data are missing on the required length of affordability for roughly one-fifth of the programs (12/55), making it difficult to identify the effect of that characteristic.

5.2 How have IZ programs affected housing prices and production?

To the extent that IZ imposes additional costs on new development, we would expect it to reduce production of new housing and increase prices of both new and existing houses, holding other factors constant. To test these hypotheses, we use panel data to estimate reduced-form models of housing prices and permits, including measures for the presence of IZ, as shown in

Equation 3.

12 Results of t-tests on mean differences in a number of characteristics (shown in Appendix A) show few systematic differences between jurisdictions that report the year IZ was adopted and those that do not (those missing year of adoption are less highly educated and more likely to target very low income households). Jurisdictions that do not report whether IZ has ever been applied tend to have larger, older populations, higher housing density, less restrictive zoning and older IZ programs. 13 Nine jurisdictions did not report the number of units produced and must be excluded from this analysis. The numbers of units were self-reported by municipal staff and have not been independently verified. In many cases it is unclear whether staff reported the number of affordable units currently in existence or the number of units ever created (which could include units with expired affordability). Since the accuracy of the exact unit counts is questionable, we ran the specifications both on the number of units as a continuous variable and as an ordered categorical variable; results are essentially the same, so we report only the estimates on the continuous measure.

(3) Permitsit = f (IZ _ yearsit , X it ,Other _ regsit , LIHTCit ,Cityi ,Yeart )

where Permitsit is a measure of housing permits (or prices) in jurisdiction i at time t, IZ_yearsit is

a set of dummy variables indicating the length of time since IZ was adopted, Xit is a vector of

housing supply and demand determinants in jurisdiction i at time t, Other_regsit is a vector of

variables measuring other types of land use regulations in jurisdiction i in time t and LIHTCit is the number of LIHTC units built in jurisdiction i as of time t. Cityi and Yeart are vectors of

fixed-effects for jurisdiction and year.

One of the main challenges to identifying the effects of IZ (and other land use

regulations) on housing prices and production is the possible confounding effects of omitted (and

sometimes unobservable) variables. In particular, if jurisdictions that adopt IZ differ

systematically from those that do not – for instance, by adopting other land use regulations or

policies that constrain development, or if their residents are more likely to use the political

process to block development through informal mechanisms – we run the risk of attributing the

effects of those other policies and practices to IZ. We include fixed effects for each jurisdiction

to help control for any characteristics of jurisdictions that do not change over time (perhaps including resident preferences over development). But if adoption of IZ is concurrent with other changes that affect housing market outcomes, such as revisions to the baseline zoning, then our estimated coefficient on the IZ variables may still be biased.

Ideally, we would also control for annual changes within jurisdictions in housing supply

and demand determinants, including other land use regulations, which could impact housing

prices and production. Because most of our control variables are drawn from the decennial

census, we can only interpolate values for the intervening years. This method should give

reasonable approximations of annual values for variables that change slowly over the decade,

such as demographic trends, but are less reliable for variables that experience large changes over

this period or have high annual variance.

We use annual permits for single-family houses as a measure of housing production in

both metropolitan areas. We chose to use single family permits because they make up the

overwhelming majority of all housing permits issued in both areas during the period from 1980 to 2005. In any given year, single-family permits average over 90 percent of total permits, and between 50 and 90 percent of jurisdictions in our sample issue no permits for multifamily housing. Using a measure of combined single-family and multifamily permits is not feasible, because the two markets display very different patterns over time and with respect to basic market determinants (for instance, multifamily permits rise in the mid-1980s before dropping off sharply after 1986, likely reflecting changes in allowed depreciation in the Tax Reform Act of

1986, while single family permits continue to rise until the early 1990s). Moreover, in the

Suburban Boston area, a large share of multifamily housing in recent years has been developed under Chapter 40B, which changes the economics of development. Because annual permits are highly variable (for instance, a large subdivision may be permitted in a single year but built over several years, in which very few new permits are issued), we construct three-year rolling averages of permits as the dependent variable.14

Using a similar logic, we use data on the sales prices of single family homes as the most relevant measure of housing costs. Most jurisdictions in our sample have very few sales in any given year of other property types for which sales data are available.15 Table 2 provides more

14 The universe of permit-issuing jurisdictions changes over time as the census adds and removes places. Thirteen places in our sample of CA jurisdictions are missing permit data for at least some years, including four places with IZ. However, all but one adopted IZ well after permit data became available, so this should not affect the results. 15 We repeat the specifications for Suburban Boston, shown in Table 5, using median price for all property sales as well. Besides single-family, two- and three-family and condos, “all properties” includes larger multifamily, commercial buildings, and vacant land sales. Several of the smaller towns have small numbers of single-family sales but substantial numbers of total sales – given the locations and characteristics of these towns, it seems likely

detailed descriptions and sources of the housing sales data for each area. Our analysis focuses on

price effects in the owner-occupied market rather than the rental market for two reasons. First,

the rental market in most jurisdictions in the sample is quite small (median owner-occupancy rate

is approximately 75-80 percent, and many jurisdictions have a small absolute number of rental

housing units), so that median rents may reflect idiosyncratic characteristics of a few large

properties. Second, the only source of data on rents is the decennial census, so effects of IZ on

rents could only be seen on a small number of widely spaced observations.

To indicate the presence of IZ in a given jurisdiction and year, we use a set of dummy

variables that indicate the length of time IZ has been in place. Because projects that started prior

to the adoption of IZ usually will be grandfathered in, we would expect some lag time before IZ

produces any effects on housing prices or permits. Conversations with developers and local

officials in several Boston area jurisdictions suggest that it takes about 2-3 years for residential

projects to be completed, therefore in the simplest specification, we use a dummy variable indicating that IZ has been in place for at least 2 years. The effects of IZ may change over time,

as developers and officials become more adept at implementing the program. In both regions,

the distribution of the number of years IZ programs have been in place is highly skewed (a small

number of programs have been in effect for long periods of time), so to accommodate the

distribution we construct a set of dummy variables indicating the length of time since IZ was

adopted. These functional forms yield more strongly significant and robust results than similar specifications using linear time trends and are more easily interpreted than specifications using the log of years adopted, which give substantively similar results. The results are quite robust to

that total sales include a number of vacant land parcels intended for residential subdivisions, a property type that should reflect price effects of IZ. Regression results using total sales prices are substantively the same as results of single-family prices, but more strongly significant. However, given the uncertainty about the composition of sales, we do not show these results here.

variations in the cutoff points used to construct dummy variables. All specifications exclude jurisdictions that do not report the year IZ was adopted. Ideally we would also like to include measures of the structural components of IZ described in Section 2 (mandatory status, density bonus, etc.). However, the sample size and limited variation across these components within each metropolitan area in our sample hinder our ability to do this.

In addition to including the interpolated controls for market determinants of housing supply and demand, in the Suburban Boston specifications we also control for adoption of several other types of land use regulations, namely cluster zoning, growth management, wetlands bylaws and septic rules. The effects of these regulations are likely to vary with the length of time they have been in place, so we control for the log of years since each regulation was adopted.16

The final control variable used in the regressions is a measure of the number of units built under the Low Income Housing Tax Credit (LIHTC) program in each year. IZ is only one of several policy tools for producing affordable housing; if the amount of affordable housing developed under other policies is correlated with the likelihood of adopting IZ and with housing market outcomes, omitting this variable may bias our estimates. Unfortunately we have no data on other local housing policies, but since LIHTC is by far the largest program for producing below-market rate housing at the national level, this should be a good proxy for the level of participation in non-IZ affordable housing production.

16 We are missing data on the year of adoption for a number of each of these regulations as well, so those jurisdictions with missing data are excluded from the regression. Unfortunately, dropping these observations excludes one third (33) of the sample jurisdictions with IZ, raising concerns about the possibility of selection bias among the remaining observations. In Appendix A, we present several robustness checks on the functional form of other land use regulations; using lagged dummies rather than log of years gives similar results.

Section 6: Regression results

6.1 Affordable housing produced under IZ in Suburban Boston and San Francisco MSA

Only two factors are robustly significant predictors of whether a jurisdiction in Suburban

Boston has produced any affordable units under its IZ program: the length of time the policy has

been in effect and the minimum lot size for single-family homes, as shown in Table 4. IZ

programs that have been in place for 5-14 years are significantly more likely to have built some

affordable housing than programs that are less than two years old, all else equal. This likely

reflects the relatively long time needed to complete a development project in the region, as well

as some learning curve for developers and local officials at implementing the program. The

other significant finding is that the probability of having used IZ decreases with the minimum lot size required for single family houses; larger lot sizes indicate more restrictive baseline zoning, which could either inhibit the ability to build affordable units directly or could indicate community preferences for lower-density, more expensive housing. Perhaps because of the small sample and restricted time frame, few of the characteristics of IZ seem to affect whether IZ has been used; the estimated coefficients on mandatory status, density bonus and buyout options are not significantly different from zero (Columns 3 and 4). The dummy indicating that IZ is triggered by cluster zoning is strongly significant and the indicator of IZ triggered by minimum size is weakly significant when controlling for market pressures and other regulations (Column

4). However, this may simply reflect that jurisdictions with cluster zoning in general are more likely to have used IZ, regardless of the trigger mechanism; the dummy on cluster zoning was omitted because of perfect multicollinearity (no jurisdiction without cluster zoning reported having used IZ). In general, given the small number of observations, the share of very recently adopted programs, and the relatively large number of places for which we are missing data on

whether IZ has ever been used, it is difficult to draw strong conclusions from these results about what programmatic, market or regulatory characteristics make it more likely that IZ programs will produce affordable units.

Production of affordable units under IZ in the San Francisco area appears to reflect somewhat more of the nuances of IZ programs, as shown in Column 1 of Table 5. Similar to the

Boston results, the number of affordable units built increases with the length of time IZ has been in effect; programs in place for at least 20 years have produced significantly more units than programs in place for fewer than 10 years. In addition, the number of units built increases as the minimum project size that triggers IZ increases. The number of units built also increases if the program provides a density bonus, although the coefficient is only weakly significant. These results suggest that less stringent programs might actually produce more affordable units, a plausible result if developers avoid jurisdictions with highly stringent programs.

Column 2 of table 5 examines the relationship between market conditions and affordable units produced. The number of units increases with the size of the black population but decreases with the size of the Hispanic population. Somewhat counter-intuitively, jurisdictions with more growth management practices produced more affordable units. The number of affordable units produced under IZ decreases as the number of LIHTC units increases, suggesting these two mechanisms may be used as substitutes. Jurisdictions with a higher share of neighboring jurisdictions in the county that also have IZ programs also produced more affordable housing under IZ, although the difference is only weakly significant.

Column 3 adds controls for market forces to IZ program characteristics. Coefficient estimates and significance levels are fairly robust, and the explanatory power of the model increases considerably. The small sample size raises two possible concerns with the results,

however; standard errors increase in small samples, decreasing the probability of observing statistically significant estimates, while some of the significant results could reflect spurious or idiosyncratic correlations of these particular jurisdictions that are not observed in the larger population. Thus these results should be interpreted with caution.

The model shown in Column 4 excludes counties, to determine whether production patterns differ between cities and counties; the most notable change is that there is no longer a statistically significant relationship between the age of the IZ program and affordable housing production. Among the four counties that report affordable units produced, there is nearly perfect correlation (0.97) between years of IZ and units produced, while the correlation between these two variables among cities and towns is relatively weak (0.21). Moreover, counties have produced on average more than three times as many affordable units over the lifetime of IZ as cities and towns, although the years of IZ adoption are roughly similar across jurisdiction types.

6.2 Effects of IZ on single-family permits and prices in Boston-area suburbs

The estimated effects of IZ on single-family permits in Suburban Boston, shown in

Columns 1-4 of Table 6, provide some evidence that IZ constrains new development, but the results are not conclusive. The simplest model, which includes a dummy variable for IZ in place at least two years as well as jurisdiction and year fixed effects, suggests that the presence of IZ is associated with roughly 10 percent fewer single-family permits per year, significant at the five percent level (Column 1). The regression using dummy variables for years IZ has been in effect gives similar results and suggests that the size of the effect increases over time: the estimated drop in permits increases from 10.9% for 5-9 year-old IZ programs to over 30% for programs older than 10 years, significant at the five-percent level (Column 2). The median number of single-family permits per year is about 35 during the time period examined, implying annual

decreases of 3 to 10 permits, depending on the year of adoption. However, the estimated coefficients on all dummy variables decrease and become statistically insignificant once controls for market forces and LIHTC units are added (Column 3). If the variables added in Model 3 are accurate estimates of the within-jurisdiction changes in market determinants of permits, these results would imply that observed differences in permits reflect changes in housing market conditions that are correlated with the adoption of IZ, rather than the effects of IZ itself. As discussed in Section 5, however, linear interpolations are imperfect measures for variables that experience large variations in annual changes over the decade, so including these variables may introduce noise or even bias into the estimates.

In the final specification on log of permits (Model 4), which adds controls for several other types of land use regulations, the absolute value of the estimated coefficient on 2-4 year old

IZ programs is unexpectedly positive and statistically significant, while the coefficients on older programs are not statistically significant. But this specification raises the concern of selection bias, because it excludes all the jurisdictions for which we do not have data on the year that the other regulations in the regression were adopted. The overall sample size and the number of jurisdictions with IZ programs drops by one-third from Model 3 to Model 4; robustness checks shown in Appendix Table B.1 suggest that the estimated coefficients are quite sensitive to the exclusion of these observations in other specifications as well.

The results in Columns 5-8 of Table 6 show slightly stronger evidence that IZ has put upward pressure on single-family home prices in Boston-area suburbs between 1987 and 2004.

The estimated coefficient on the dummy for IZ programs at least 2 years old (Column 5) suggests that adopting IZ is associated with a 2.8 percent increase in prices, controlling for jurisdiction and year fixed effects. Prices in jurisdictions with IZ programs in place for 5-14

years are 3.75-3.95 percent higher prices than prices in jurisdictions with very young or no IZ

programs, with weak evidence of slightly higher prices in jurisdictions with 2-4 year old IZ

programs (Column 6). Adding controls for population size and interpolated changes in other

local demographics (Column 7) decreases the magnitude and statistical significance on all the IZ

age coefficients; only the 5-9 year category is still statistically significant at the ten percent level.

These results do not change much with the addition of controls for other types of regulations

(Column 8), and again the sample size decreases sharply due to missing data. The coefficient from Model 7 suggests that jurisdictions with 5-9 year old IZ programs saw a price increase of two percent, compared to not having IZ; applying this estimate to the real median single family sales price in 2000, about $243,000, implies a price increase of roughly $4860 associated with a relatively mature IZ program.

6.3 Effects of IZ on single-family permits and prices in San Francisco area

The analysis shows no evidence of a statistically significant effect of IZ on either single-

family permits or single-family housing prices in the San Francisco area. Columns 1-3 of Table

7 show regression results of the effect of IZ on permits, using both a simple indicator of any IZ

in place for two or more years and a set of dummy variables for age of IZ programs, as well as

jurisdiction and year fixed effects and, in Column 3, controls for various housing market

determinants. Only one coefficient on any of the IZ indicators is statistically different from zero

– in Column 3, IZ programs in place for 20 or more years becomes positive and weakly

significant when adding housing market controls. However, because the sign is actually the reverse of the expected direction and is only marginally significant, it seems unlikely that this result is robust or substantively meaningful. None of the regressions modeling the effect of IZ on housing prices (Columns 4-6) yield any statistically significant coefficients. We tried a

number of other specifications including indicators of the various components of IZ programs

(density bonus, required share of affordable units, minimum project size, targeting very-low income households), but the coefficients were not significant in any of those specifications.

Based on the data available, it does not appear that the adoption of IZ among jurisdictions in the

Bay Area has produced systematic effects on housing prices or production.

Several problems with the data suggest that those results should be interpreted cautiously, especially in light of both the theoretical predictions from the economic models and the results from Suburban Boston. One serious concern is that the identification strategy relies on the year

IZ was adopted; as described in Section 4, the various surveys of IZ do not always agree on the year of adoption. If some of the dates used in the regressions are just random errors, or mistakenly report the dates that the jurisdictions adopted informal or less stringent precursors to the programs currently in place, this variable will be an imperfect measure and will be less likely to yield significant results. In addition, the regressions provide estimates of the average effect of

IZ across all jurisdictions; if the effects of IZ vary among jurisdictions, either because of differences in how IZ programs are structured, how they are implemented, or interactions with different economic or political conditions in the particular location, then the average may obscure the effects of some types of IZ. For instance, as mentioned in Section 5, some jurisdictions may adopt IZ in order to fulfill state regulatory requirements, but may have little interest in enforcing the policies once they are on the books. Those jurisdictions will see little effect from IZ (and bring down the average effect for the entire data set), not because IZ has no effect on the supply or price of housing, but because the IZ is not enforced.

Section 7: Conclusions and future research

In this study, we examine the characteristics of local IZ programs in the San Francisco metropolitan area and Suburban Boston, and analyze two important questions: what market conditions and characteristics of IZ programs affect the production of affordable housing units under IZ; and how has IZ impacted overall housing prices and production. Below we briefly summarize the results of our research and outline some areas for future research.

The descriptive statistics reveal considerable diversity in the structure and characteristics of IZ programs, both within and across the two regions examined. Nearly half the jurisdictions in the San Francisco area have IZ and the median program has been in place for 15 years. In

California, most IZ ordinances are mandatory and apply broadly to all residential development, with only a few exemptions. However, alternatives to on-site construction, such as fees or land in-lieu, are widely offered, as are density bonuses or other cost offsets. IZ is equally widespread in Suburban Boston but many programs have only come into effect in the past five years. IZ programs in the Boston area are more narrowly written than in California; rather than applying to most residential construction, IZ is often triggered by development proposals in certain locations, structure types or in combination with cluster zoning.

There also is considerable variation across the two regions in affordable housing production under IZ. Nearly all jurisdictions with IZ in the Bay Area have produced some affordable housing under the program; the median jurisdiction has built 85 units over the program’s existence, or roughly seven units per year. Across all jurisdictions in the area, 9,154 affordable units had been built as of 2003 through IZ. To put this in the context of other affordable housing production programs, 29,636 affordable units have been built in the Bay Area under the Low Income Housing Tax Credit (LIHTC) program between its inception in 1987 and

2003, implying annual production rates for LIHTC of about 1800 units.17 In San Francisco, production of IZ units amounts to roughly 2-3 percent of total housing production over the past

25 years. IZ has been less effective at producing affordable housing in; nearly half of Boston- area suburbs with IZ report that no affordable units have been built, although this probably reflects the recent adoption of many programs. The results of regression analyses suggest that in both regions, the amount of affordable housing produced under IZ is largely a function of the length of time that IZ has been in place. In the Boston area, jurisdictions with large single- family minimum lot sizes are less likely to have developed any affordable housing under IZ.

The San Francisco results suggest that more flexible programs may produce more affordable units.

Our analysis of how IZ has impacted housing prices and permits offers mixed evidence on whether IZ constrains housing supply. Results of regression analyses for the Boston-area suburbs do provide some evidence that IZ has increased prices and lowered production, although the estimated effect is relatively small. The estimated effect of IZ on sales prices of single- family homes is positive in all specifications and at least marginally significant in most models.

The estimated effect of IZ on single-family permits in Suburban Boston is negative and statistically significant in some specifications, but the magnitude and significance decrease in the specifications that control for other regulations and demographic changes. The analysis of IZ in the Bay Area shows no evidence of statistically significant effects of IZ on production levels or sales prices of existing single-family homes.

One concern that arises in identifying the effects of IZ in both regions is the difficulty of defining clear treatment and control groups. Both California and Massachusetts have statewide

17 Data on production of LIHTC units is available online at http://www.huduser.org/datasets/lihtc.html#data. Some units built under IZ may receive subsidies through LIHTC.

laws that may encourage more jurisdictions to adopt IZ than would do so in the absence of the laws, while at the same time establishing mechanisms for jurisdictions without local IZ programs to develop affordable housing. This suggests that some of the control jurisdictions may operate as though they have informal IZ programs, with similar effects on housing supply, while some of the treatment jurisdictions may have IZ programs on the books that are seldom used or not rigorously enforced. If this is the case, then the relevant question may not be whether having an

IZ policy on the books raises prices and constrains supply, but whether the jurisdiction actively requires (or offers incentives for) affordable housing through any mechanism.

Because of the difficulty in collecting systematic data on enforcement of IZ or alternative affordable housing mechanisms, it may be more feasible to develop a better understanding of the various motives that prompt jurisdictions to adopt IZ (or comparable informal policies). Our models implicitly assume that IZ is a response to market conditions, such as past or anticipated increases in housing prices. However, the demand by local residents for land use regulation also may reflect more complex political, social or institutional factors, such as the desire for economically or ethnically homogenous neighbors, and aesthetic or environmental preferences over the timing, location and type of development. Some of these factors are likely to be captured by our control variables (for instance, the racial and ethnic heterogeneity of the current population is likely to be a reasonable predictor of racial exclusive preferences). And many of the institutional or political factors are likely to remain fairly constant over time, so will be absorbed by the jurisdiction fixed effects in the models on housing permits and prices.

Nonetheless, we recognize that our models may be omitting important political or social preferences that affect both the likelihood of adopting IZ and how effective IZ is at producing

affordable housing. Developing a better understanding of the political economy of IZ is thus an important area for future research.

Finally, there are two particular characteristics of our study areas that may explain the relatively small effects of IZ on housing markets. First, both the Bay Area and Suburban Boston are widely acknowledged to have highly restrictive regulatory environments for housing

development, and IZ is only one of many policies (and a fairly recent one) that are likely to

affect housing production and prices. Thus the marginal effect of IZ is unlikely to be very large,

compared to the cumulative effect of all regulations. Second, the effects of IZ and other types of

regulations may be fairly small compared to the market determinants of supply and demand,

such as changes in population size, income, or costs of labor and building materials. IZ

programs were most widely adopted in the Bay Area jurisdictions during the 1990s and in

Suburban Boston after 2000, both of which represented periods of extremely strong housing

demand in the respective regions. Thus developers may well have been more willing and able to

provide affordable units while still enjoying larger profits than would have been possible in a

weaker housing market. If housing markets continue to soften in the months to come, as a result

of fallout from the mortgage foreclosure crisis, the environment may be less hospitable for

inclusionary zoning, and IZ programs may have more significant impacts on local housing

markets.

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40 Table 1: Characteristics of IZ programs in Bay Area and Suburban Boston San Francisco MSA Suburban Boston 6/9 counties Prevalence of IZ 99/187 cities/towns 49/105 cities/towns Year adopted Median 1992 2001 Range 1973-2006 1972-2004

Mandatory 93% 58% Limited eligibility Exemptions Broadly applicable Broad exemptions Buyouts 86% 38% % affordable required (median) 15% 15% Density bonus 67% 71% Very low, low & mod Low Income targets Low & mod Low & mod 1/3 require permanent Affordability Median 45 yrs Half don't specify Counties: 15 units Median annual production 43% produced no units Cities/towns: 6 units

Table 2: Variable definitions and sources

Variable Definition/source IZ variables – Boston-area Source: LHR database suburbs IZ = 1 if jurisdiction has adopted IZ by 2004. Also included as dummy variable for IZ lagged by two years. Log(years IZ) Log(Number of years since IZ adopted) Mandatory = 1 if IZ program is mandatory, 0 if optional Density bonus = 1 if IZ offers density bonus, 0 otherwise Buyout options = 1 if IZ includes options besides on-site construction, 0 otherwise Minimum size trigger = 1 if IZ triggered by minimum project size, 0 otherwise Cluster trigger = 1 if IZ triggered by cluster zoning, 0 otherwise IZ year adopt missing = 1 if data missing on year IZ adopted IZ ever applied = 1 if IZ program applied by 2004, 0 otherwise

IZ variables – San Francisco Source: CA Coalition/NHC of CA, Furman Center survey IZ = 1 if jurisdiction has adopted IZ by 2006. Mandatory = 1 if IZ is mandatory Density bonus = 1 if IZ offers density bonus Number of buyout options Number of buyout options (4 maximum) Min project units Minimum project size needed to trigger IZ Min % affordable Pct affordable units required Some units target VLI = 1 if some units targeted at very low income households Years affordable Required number of years affordable Years IZ in place Years since IZ adopted Affordable units Number of units produced 1 = < 20 units; 2 = 21-100; 3 = 101-250; 4 = 251+ Pct in county w/ IZ % of jurisdictions in county with IZ Avg year IZ adopted, county Average year IZ adopted within county

Housing market outcomes – all areas SF permits Annual single-family units permitted (1980-2006) Source: Census New Residential Construction series Prices – Boston-area suburbs Median sales price, single-family homes in constant 2000$ (annual, 1987-2004) Source: Banker and Tradesman TownStats Prices – San Francisco Median sales price, existing single-family homes in constant 2000$ (annual, 1988-2006). Data on 8 pairs of cities are reported jointly. Source: Data Quick Demographic and other control variables – all areas Log(pop) Log of population (1970, 1980-2006). Intermediate and subsequent years linearly interpolated/extrapolated. Source: All demographic variables taken from decennial census. % change pop Percent change population, 1970-1980 42

% change price Percent change in housing prices, 1970-1980 Pct BA, post-grad % of population with college, graduate degrees. Linearly interpolated/extrapolated between census years. Pct non-Hisp white % of population, white non-Hispanic. Pct non-Hisp black % population black, non-Hispanic Pct non-Hisp Asian % population Asian, non-Hispanic Pct Hispanic % population Hispanic Pct < 18 yrs % of population < 18 years. Housing density Housing units/land area. Log(area) Log of land area. Distance to Boston, Distance to Distance to Boston (miles), distance squared. Calculated Boston^2 using lat-long coordinates from centroid of each jurisdiction. Distance to San Francisco Distance (miles) to San Francisco Distance to San Jose Distance (miles) to San Jose County, City = 1 if jurisdiction is a county or city; town is omitted category Other land use regulations – Boston-area suburbs Pct in county w/ IZ % of jurisdictions in county with IZ. Source: All data on Boston regulations from LHR. Log(SF lot size) Log of average single-family minimum lot size (2004). Log(MF lots) Log of potential MF lots allowed under zoning (2004). Cluster = 1 if cluster zoning allowed, 0 otherwise Growth = 1 if annual cap on permits or subdivision phasing. Wetlands bylaw = 1 if jurisdiction has adopted local wetlands bylaw. Septic rules = 1 if jurisdiction has adopted septic regulations.

Table 3: Variable summary statistics

Area Variable Mean Std. Dev. N Boston-area suburbs IZ 0.529 0.500 187 Years IZ 6.061 7.792 99 Mandatory 0.576 0.497 99 Density bonus 0.707 0.457 99 Buyout options 0.384 0.489 99 Minimum size trigger 0.150 0.358 99 Cluster trigger 0.176 0.382 99 IZ year adopt missing 0.091 0.288 99 IZ ever used 0.338 0.477 65 SF permits 51.57 56.92 187/yr SF price 248,456 112,390 187/yr Pop 21,575 22,158 187/yr % change pop 0.109 0.228 187/yr % change price 0.219 0.126 187/yr Pct BA, post-grad 27.17 16.23 187/yr Pct non-Hisp white 95.60 6.54 187/yr Pct < 18 yrs 28.80 6.43 187/yr Hsg density 1.07 1.72 187/yr Area 11,309 7180 187 Distance to Boston 22.54 9.94 187 Pct in county w/ IZ 52.94 16.74 187 SF lot size 40,031 21,887 187 MF lots 4172 8168 187 Cluster 0.802 0.399 187 Growth 0.289 0.454 187 Wetlands bylaw 0.701 0.459 187 Septic rules 0.583 0.494 187 San Francisco CMSA IZ .48 .502 113 Mandatory .927 .269 52 Density bonus .70 .454 50 Number of buyout options 2.0 1.31 56 Min project units 5.33 5.57 54 Min % affordable 13.56 4.39 54 Some units target VLI .554 .502 56 Yrs affordable 55.36 27.15 42 Years IZ in place 12.87 7.85 55 Affordable units 199 289 46 Pct in county w/ IZ 49.56 19.55 113 Avg year IZ adopted, county 1993 4.9 113 Population 82,208 195,683 113 County government .080 .272 113 Pct BA, post-grad 27.6 15.1 113 Pct non-Hispanic black 4.87 9.43 113 44

Pct non-Hispanic Asian 5.75 5.41 113 Pct Hispanic 10.11 8.69 113 Housing density .0005 .0004 113 Land area 2.00e+08 6.25e+08 113 Distance to San Francisco 29.3 16.6 113 Distance to San Jose 43.1 24.4 113 Price, single-family existing 439,692 283,628 2146 Single-family permits 152 282 2870

Table 4: Determinants of affordable housing production under IZ, Boston-area suburbs Dependent variable: IZ ever used (2004) Variable: (1) (2) (3) (4) IZ 2-4 yrs 0.118 0.269 (0.175) (0.177) IZ 5-14 yrs 0.465*** 0.706*** (0.117) (0.053) IZ 15+ yrs 0.143 0.276** (0.218) (0.109) IZ mandatory? -0.036 0.132 (0.162) (0.100) Density bonus? -0.111 -0.244 (0.133) (0.199) Buyout options 0.119 0.164 (0.088) (0.113) Minimum size triggers IZ? -0.083 -0.232** (0.205) (0.098) Cluster triggers IZ? 0.172 0.256*** (0.159) (0.097) Yr missing -0.060 0.207 (0.221) (0.244) Log(pop) 0.162** 0.118 0.171 (0.063) (0.136) (0.147) % change pop, 1970-80 -0.293 -0.379 -0.391 (0.361) (0.606) (0.301) % change hsg prices, 1970-80 0.457 1.078 2.112 (0.681) (1.128) (1.324) Pct BA, post-grad 0.007 0.008 0.006 (0.005) (0.006) (0.006) Pct non-Hispanic white 0.005 -0.003 -0.005 (0.028) (0.030) (0.020) Pct < 18 yrs 0.003 0.027 0.062*** (0.022) (0.020) (0.015) Housing density 0.026 -0.019 0.055 (0.084) (0.085) (0.034) Log(area) 0.067 0.154 0.285*** (0.149) (0.125) (0.072) Distance to Boston, miles 0.003 -0.002 -0.007 (0.009) (0.011) (0.006) Pct in county w/ IZ -0.001 -0.007 (0.006) (0.006) Log(SF lot size) -0.234** -0.258** (0.116) (0.113) Log(MF lots) 0.034 0.055* (0.027) (0.031) Any growth mgt 0.025 0.075 (0.111) (0.096) Has wetlands bylaw -0.006 -0.393* (0.161) (0.208) Has septic rules -0.145 -0.462*** (0.178) (0.113) LIHTC units, 1990 -0.001 -0.003** (0.002) (0.001) Observations 65 64 64 64 R-squared 0.119 0.091 0.140 0.326 Robust standard errors, clustered by county, in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%

Table 5: Determinants of IZ adoption & affordable housing production, San Francisco Dependent variable: Log(affordable units) (1) (2) (3) (4) IZ 10-14 years 0.280 0.470 0.268 (0.764) (0.699) (0.725) IZ 15-19 years 0.058 1.349** 1.016 (0.693) (0.443) (0.732) IZ 20+ years 1.513** 1.634* 1.244 (0.526) (0.741) (0.853) Density bonus? 0.830* 0.563* 0.366 (0.370) (0.300) (0.308) Number of buyout options 0.219 0.288 0.215 (0.251) (0.184) (0.136) Min project units 0.143*** 0.106*** 0.119*** (0.037) (0.020) (0.018) Min % affordable -0.062 0.048 0.012 (0.038) (0.043) (0.058) Some units target VLI -0.209 -0.702** -0.733** (0.470) (0.238) (0.250) Log(pop) -0.917 -0.159 0.231 (1.349) (1.100) (0.942) Pct BA + -0.023 -0.014 -0.017 (0.015) (0.024) (0.024) Pct black non-Hispanic 0.048* 0.052*** 0.048*** (0.025) (0.010) (0.012) Pct Asian non-Hispanic -0.041 -0.025 -0.015 (0.032) (0.029) (0.023) Pct Hispanic, all race -0.056** -0.055 -0.066** (0.018) (0.031) (0.027) Pct < 18 yrs 0.061 0.106** 0.099** (0.036) (0.039) (0.038) Housing density 2152 1774 1155 (1850) (1578) (1175) Growth mgt index 0.292*** 0.198** 0.169 (0.071) (0.077) (0.095) LIHTC units, 1990 -0.251*** -0.352** -0.238* (0.070) (0.114) (0.109) Log(area) 1.432 1.020 0.546 (1.115) (1.178) (1.052) Distance to San Fran 0.027 0.040** 0.045** (0.017) (0.014) (0.015) Distance to San Jose -0.024 0.003 0.003 (0.014) (0.010) (0.009) Pct in county w/ IZ 0.042* 0.010 0.005 (0.020) (0.014) (0.011) County -3.010 -3.144 (2.375) (3.023) Observations 45 46 45 42 R-squared 0.427 0.548 0.794 0.783 Robust standard errors clustered by county in parentheses. Column 4 excludes counties. *significant at 10%; ** significant at 5%; *** significant at 1%

47 Table 6: Effects of IZ on housing permits and prices, Boston-area suburbs Dependent variable: Log(permits, 1980-2006) Log(prices, 1987-2004) Variable: (1) (2) (3) (4) (5) (6) (7) (8) IZ 2+ years -0.102** 0.028*** (0.039) (0.006) IZ 2-4 yrs -0.001 0.066 0.134*** 0.019* 0.006 0.021 (0.049) (0.040) (0.021) (0.008) (0.010) (0.012) IZ 5-9 yrs -0.109** -0.005 0.021 0.040** 0.020* 0.023* (0.041) (0.039) (0.083) (0.005) (0.009) (0.010) IZ 10-14 yrs -0.303** -0.155 -0.087 0.038** 0.015 0.016 (0.091) (0.097) (0.215) (0.013) (0.013) (0.013) IZ 15+ yrs -0.322** -0.092 0.018 0.032 0.004 0.019 (0.101) (0.071) (0.188) (0.024) (0.021) (0.014) Log(pop) 0.525** 0.799*** 0.082 0.054 (0.150) (0.082) (0.114) (0.113) Pct BA + -0.016** -0.015* 0.007*** 0.007** (0.004) (0.007) (0.002) (0.002) Pct white -0.005 0.002 0.003*** 0.004*** (0.009) (0.013) (0.001) (0.001) Pct < 18 -0.019 -0.017 0.003 0.007 (0.024) (0.032) (0.005) (0.005) Hsg units/acre -0.537 -0.551** 0.214** 0.241** (0.357) (0.209) (0.065) (0.067) Pct towns in county w/ IZ -0.003 -0.006* -0.001 -0.001 (0.002) (0.003) (0.001) (0.001) Any LIHTC? -0.122 -0.054 -0.007 0.008 (0.088) (0.127) (0.016) (0.021) Log(yrs cluster zoning) 0.088* 0.006 (0.037) (0.017) Log(yrs growth controls) 0.009 -0.012 (0.082) (0.017) Log(yrs wetlands bylaw) -0.094** -0.010 (0.034) (0.007) Log(yrs septic regs) -0.095** -0.021*** (0.028) (0.003) City/town FEs Y Y Y Y Y Y Y Y Year FEs Y Y Y Y Y Y Y Y Observations 4590 4590 4590 3051 2703 2703 2703 1785 R-squared 0.763 0.765 0.773 0.768 0.968 0.968 0.970 0.972 Robust standard errors clustered by county in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1% The decreased sample size in models (4) and (8) are caused by missing data on the year of adoption for cluster zoning, growth controls, wetlands bylaws and septic regulations. Model 4 reflects data on 113 jurisdictions, model 8 includes 110 jurisdictions; the other models reflect data for 166 jurisdictions (observations missing data on the year IZ was adopted are excluded from all models). Regressions on prices exclude all observations with fewer than 50 sales in a given year.

Table 7: Effects of IZ on housing permits and prices, San Francisco Dependent variable: Log(Permits 1980-2006) Log(Prices 1988-2006) Variable: (1) (2) (3) (4) (5) (6) IZ 2+ years 0.069 0.013 (0.066) (0.010) IZ 2-9 years 0.070 0.118 0.013 0.001 (0.075) (0.123) (0.011) (0.007) IZ 10-19 years 0.111 0.199 0.023 0.007 (0.135) (0.192) (0.022) (0.021) IZ 20+ years 0.226 0.405* 0.031 0.017 (0.206) (0.215) (0.029) (0.025) Pct in county w/ IZ -0.004 0.001 (0.005) (0.001) Log(pop) 0.240 0.022 (0.188) (0.012) Pct BA + -0.008 -0.002 (0.015) (0.002) Pct black -0.004 -0.0003 (0.012) (0.0028) Pct Asian -0.008* 0.001 (0.004) (0.002) Pct Hispanic 0.025 0.002 (0.016) (0.002) Pct < 18 years 0.018 0.001 (0.019) (0.003) Housing units/acre -2053* 423.8* (984.4) (202.9) Log(LIHTC units) -0.027 -0.0002 (0.032) (0.0019) Jurisdiction FEs Y Y Y Y Y Y Year FEs Y Y Y Y Y Y Observations 2870 2870 2870 2072 2072 2072 R-squared 0.806 0.806 0.816 0.977 0.977 0.977 Robust standard errors clustered by county in parentheses * significant at 10%; ** significant at 5%; *** significant at 1%

Appendix A: Robustness checks on missing data, Boston

Table A.1 Differences between reporting and non-reporting jurisdictions, Boston Year IZ adopted IZ ever used Not Not Variable Missing missing Difference Missing missing Difference n IZ program characteristics Mandatory 0.50 0.59 -0.09 0.60 0.56 0.04 99 Density bonus 0.67 0.72 -0.05 0.71 0.70 0.01 99 Buyout options 0.22 0.42 -0.20 0.49 0.33 0.16 99 Number IZ triggers 1.06 1.27 -0.21 1.29 1.20 0.08 99 Min project size 0.17 0.31 -0.14 0.37 0.23 0.14 99 Cluster trigger 0.33 0.33 0.00 0.34 0.33 0.02 99 District trigger 0.11 0.12 -0.01 0.11 0.13 -0.01 99 Structure trigger 0.28 0.16 0.12 0.14 0.20 -0.06 99 Yrs affordable 78.6 74.9 3.6 63.1 81.1 -18.0* 51 Income target 20.4 14.2 6.2** 15.2 15.3 -0.1 72 IZ ever used 0.20 0.35 -0.15 Ever used missing 0.44 0.33 0.11 Year IZ adopted 1993 1998 -4.8** 81 Year missing 0.23 0.16 0.07 99 Demographics/location Population 13,924 23,524 -9,600 31,227 16,762 14,465*** 99 Pct BA plus 20.1 27.8 -7.7** 25.7 26.9 -1.3 99 Pct white 95.9 97.0 -1.1 96.2 19.2 77.0 99 Pct < 18 29.7 28.5 1.2 26.7 29.8 -3.1*** 99 Housing density 0.8 1.1 -0.3 1.9 0.6 1.2*** 99 Distance Boston 25.8 21.7 4.1 20.8 23.2 -2.4 99 Other regulations Pct in county with IZ 53.0 59.3 -6.3 60.4 57.0 3.4 99 SF min lot size 45,664 39,828 5,836 34,346 44,377 -10,031** 99 # MF lots 2,700 5,819 -3,119 8,147 3,717 4,430** 99 Cluster zoning 0.88 0.96 -0.08 0.94 0.95 -0.01 99 Growth caps 0.41 0.30 0.11 0.34 0.31 0.03 99 Wetlands bylaw 0.76 0.78 -0.02 0.66 0.84 -0.18** 99 Septic rules 0.71 0.61 0.10 0.51 0.69 -0.18* 99 *, ** and *** denote statistical significance of two-tailed t-tests at 10%, 5% and 1% levels, respectively

Appendix B: Robustness tests on functional form of other regulations, Boston

Table B.1 Robustness checks on single-family permits Dependent variable: Log(permits, 1980-2006) Variable: (1) (2) (3) (4) IZ 2-4 yrs 0.073 0.137*** 0.138*** 0.132*** (0.043) (0.015) (0.016) (0.022) IZ 5-9 yrs -0.073 0.024 0.020 0.018 (0.080) (0.061) (0.075) (0.087) IZ 10-14 yrs -0.227 -0.084 -0.097 -0.095 (0.174) (0.205) (0.208) (0.230) IZ 15+ yrs -0.229 -0.004 0.010 0.007 (0.179) (0.176) (0.192) (0.205) Log(pop) 0.690*** 0.748*** 0.805*** (0.127) (0.104) (0.084) Pct BA + -0.017** -0.016* -0.015* (0.007) (0.007) (0.007) Pct white 0.002 0.0024 0.003 (0.009) (0.010) (0.011) Pct < 18 -0.020 -0.019 -0.017 (0.034) (0.034) (0.032) Hsg units/acre -0.529 -0.574* -0.585** (0.297) (0.240) (0.239) Pct towns in county w/ IZ -0.005 -0.006 -0.006 (0.003) (0.003) (0.003) Cluster zoning (2-yr lag) 0.117*** (0.024) Growth controls (2-yr lag) -0.062 (0.117) Wetlands bylaw (2-yr lag) -0.171** (0.047) Septic rules (2-yr lag) -0.115** (0.038) Log(yrs cluster zoning) 0.086** (0.033) Log(yrs growth controls) 0.007 (0.082) Log(yrs wetlands bylaw) -0.096** (0.036) Log(yrs septic regs) -0.093*** City/town FEs Y Y Y Y Year FEs Y Y Y Y Observations 3051 3051 3048 3051 R-squared 0.753 0.764 0.771 0.768 Robust standard errors clustered by county in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%

Table B2: Robustness tests on single-family housing prices Dependent variable: Log(prices, 1987-2004) Variable: (1) (2) (3) (4) IZ 2-4 yrs 0.029** 0.018 0.021 0.021 (0.009) (0.011) (0.014) (0.013) IZ 5-9 yrs 0.041*** 0.022 0.023* 0.023* (0.010) (0.015) (0.011) (0.010) IZ 10-14 yrs 0.036*** 0.017 0.017 0.017 (0.009) (0.010) (0.011) (0.014) IZ 15+ yrs 0.052*** 0.021* 0.021* 0.020 (0.012) (0.010) (0.010) (0.013) Log(pop) 0.018 0.040 0.052 (0.111) (0.124) (0.112) Pct BA + 0.007** 0.007** 0.007** (0.002) (0.002) (0.002) Pct white 0.004*** 0.004*** 0.004*** (0.001) (0.001) (0.001) Pct < 18 0.007 0.007 0.006 (0.005) (0.005) (0.005) Hsg units/acre 0.267** 0.256** 0.245** (0.073) (0.077) (0.067) Pct towns in county w/ IZ -0.001 -0.001 -0.001 (0.001) (0.001) (0.001) Cluster zoning (2-yr lag) -0.006 (0.023) Growth controls (2-yr lag) -0.011 (0.032) Wetlands bylaw (2-yr lag) -0.009 (0.010) Septic rules (2-yr lag) -0.020** (0.007) Log(yrs cluster zoning) 0.006 (0.017) Log(yrs growth controls) -0.012 (0.017) Log(yrs wetlands bylaw) -0.010 (0.007) Log(yrs septic regs) -0.021*** (0.003) City/town FEs Y Y Y Y Year FEs Y Y Y Y Observations 1785 1785 1785 1785 R-squared 0.969 0.972 0.972 0.972 Robust standard errors clustered by county in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%